scholarly journals Network modeling of single-cell omics data: challenges, opportunities, and progresses

2019 ◽  
Vol 3 (4) ◽  
pp. 379-398 ◽  
Author(s):  
Montgomery Blencowe ◽  
Douglas Arneson ◽  
Jessica Ding ◽  
Yen-Wei Chen ◽  
Zara Saleem ◽  
...  
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Network Modeling ◽  
Dynamic Networks ◽  
Cell Interaction ◽  
Omics Data ◽  
Powerful Approach ◽  
Challenges And Opportunities ◽  
Cell Gene ◽  
Cell Networks

Abstract Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

scholarly journals Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

2020 ◽  
Author(s):  
HARIPRIYA HARIKUMAR ◽  
Thomas P Quinn ◽  
Santu Rana ◽  
Sunil Gupta ◽  
Svetha Venkatesh
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Single Cells ◽  
Biological Data ◽  
Patient Specific ◽  
Expression Data ◽  
Drug Responses ◽  
Dual Channel ◽  
Cell Gene ◽  
Cell Networks

Abstract Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods: We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

scholarly journals Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Haripriya Harikumar ◽  
Thomas P. Quinn ◽  
Santu Rana ◽  
Sunil Gupta ◽  
Svetha Venkatesh
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Single Cells ◽  
Biological Data ◽  
Patient Specific ◽  
Expression Data ◽  
Drug Responses ◽  
Dual Channel ◽  
Cell Gene ◽  
Cell Networks

Abstract Background The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. Methods We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. Conclusions Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

scholarly journals Interpretable generative deep learning: an illustration with single cell gene expression data

2022 ◽  
Author(s):  
Martin Treppner ◽  
Harald Binder ◽  
Moritz Hess
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Latent Variables ◽  
Generative Models ◽  
Omics Data ◽  
Expression Data ◽  
Cell Gene Expression ◽  
The Relationship ◽  
Cell Gene

AbstractDeep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

scholarly journals Recent advances in trajectory inference from single-cell omics data

2021 ◽  
Author(s):  
Louise Deconinck ◽  
Robrecht Cannoodt ◽  
Wouter Saelens ◽  
Bart Deplancke ◽  
Yvan Saeys
Keyword(s):  
Single Cell ◽  
Omics Data ◽  
Recent Advances

scholarly journals Single-cell transcriptomics following ischemic injury identifies a role for B2M in cardiac repair

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bas Molenaar ◽  
Louk T. Timmer ◽  
Marjolein Droog ◽  
Ilaria Perini ◽  
Danielle Versteeg ◽  
...  
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cardiac Remodeling ◽  
Cardiac Injury ◽  
Ischemic Injury ◽  
Cell Types ◽  
Repair Process ◽  
Cardiac Repair ◽  
Cellular Heterogeneity ◽  
Intercellular Signaling

AbstractThe efficiency of the repair process following ischemic cardiac injury is a crucial determinant for the progression into heart failure and is controlled by both intra- and intercellular signaling within the heart. An enhanced understanding of this complex interplay will enable better exploitation of these mechanisms for therapeutic use. We used single-cell transcriptomics to collect gene expression data of all main cardiac cell types at different time-points after ischemic injury. These data unveiled cellular and transcriptional heterogeneity and changes in cellular function during cardiac remodeling. Furthermore, we established potential intercellular communication networks after ischemic injury. Follow up experiments confirmed that cardiomyocytes express and secrete elevated levels of beta-2 microglobulin in response to ischemic damage, which can activate fibroblasts in a paracrine manner. Collectively, our data indicate phase-specific changes in cellular heterogeneity during different stages of cardiac remodeling and allow for the identification of therapeutic targets relevant for cardiac repair.

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